实验五第二题

实验五/test2.py

@@ -7,3 +7,39 @@
 # 6、计算分水岭：根据合并图像，应用分水岭变换算法来计算分水岭，即将图像分割成不同的分水岭区域。
 # 7、降噪和后处理：对分割结果进行降噪和后处理，例如去除小的分割区域、填充空洞等，以获得最终的分割结果。
 
+import cv2 as cv
+import numpy as np
+
+img = cv.imread('test1.png', cv.IMREAD_GRAYSCALE)
+img = cv.blur(img, (5, 5))
+img = cv.convertScaleAbs(img, alpha=1.0, beta=2.0)
+cv.imshow('origin', img)
+
+grad_x = cv.Sobel(img, cv.CV_32F, 1, 0, ksize=3)
+grad_y = cv.Sobel(img, cv.CV_32F, 0, 1, ksize=3)
+grad = cv.magnitude(grad_x, grad_y)
+
+ret, markers = cv.threshold(grad, 50, 255, cv.THRESH_BINARY)
+
+markers = np.uint8(markers)
+dist_transform = cv.distanceTransform(255 - markers, cv.DIST_L2, 5)
+
+combined = cv.subtract(dist_transform, grad)
+
+image_rgb = cv.cvtColor(cv.imread('test1.png'), cv.COLOR_BGR2RGB)
+markers = np.int32(markers)
+markers[grad == 0] = 0
+markers += 1
+cv.watershed(image_rgb, markers)
+
+markers = np.where(markers == -1, 255, markers)
+markers = np.uint8(markers)
+
+output = np.zeros_like(image_rgb)
+output[markers == 255] = [0, 0, 255]
+output[markers == 1] = [18, 0, 230]
+output[markers == 0] = [255, 255, 255]
+
+cv.imshow('Segmentation Result', output)
+cv.waitKey(0)
+cv.destroyAllWindows()